Progression analytics system

ABSTRACT

A method of identifying insights related to the occurrence of an adverse health outcome of interest, comprises extracting electronic clinical data associated with historical healthcare encounters. The method also comprises defining patient groups based upon similar data patterns present in the extracted electronic clinical data wherein the patient groups have varying likelihood for the adverse health outcome. Still further, the method comprises deriving hypothesized etiological explanations for why one or more patient groups have higher likelihood when compared to other patient groups. Optionally, the method comprises identifying clinical interventions that are intended to reduce the likelihood of the adverse outcome for certain patient groups.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/US2014/030514, filed Mar. 17, 2014, entitled “PROGRESSION ANALYTICSSYSTEM”, which claims the benefit of U.S. Provisional Patent ApplicationSer. No. 61/789,695, filed Mar. 15, 2013, entitled “CLINICAL PROGRESSIONANALYTICS SYSTEM”, the disclosures of which are incorporated herein byreference.

BACKGROUND

The present disclosure relates in general to development and analytictools for use in the health care industry that can be utilized forretrospective processing and analysis of medical information.

Many clinical decisions must be made in the typical course of treating apatient who is undergoing medical care. Oftentimes these decisionsaffect the overall health and well-being of the patient. Particularly,despite current efforts to apply accepted best practices, it is possiblefor a patient that receives medical care to suffer from an adversehealth outcome. Adverse health outcomes originate in many ways which areoften occult (latent potential) in the earliest stages of a clinicalcare process. Examples include iatrogenesis, nosocomial infections,patient safety procedural failures, as well as the natural uncheckedprogression of a pathologic process or the simple confluence of untowardeffects. Further, an adverse outcome may arise in response to, or as aresult of, a treatment or procedure designed to treat a diagnosedcondition not directly related to the adverse outcome. The occurrence ofadverse health outcomes affects the overall burgeoning cost ofhealthcare.

BRIEF SUMMARY

According to aspects of the present disclosure, a method of identifyinginsights related to outcomes is provided. The method comprisesidentifying a patient-care related outcome of interest. The method alsocomprises extracting electronic clinical data associated with historicalhealthcare encounters for a plurality of patients, where the pluralityof patients include a first subset of patients that experienced theoutcome of interest and a second subset of patients that did notexperience the outcome of interest. The method further comprisesdefining patient groups based upon similar data patterns present in theextracted electronic clinical data, where the data patterns are selectedsuch that the defined patient groups differentiate from one another interms of a likelihood of the outcome of interest, consequencesassociated with the outcome of interest or both. The method stillfurther comprises deriving hypothesized etiological explanations for whyone or more patient groups have a different likelihood, consequence orboth, with respect to the outcome of interest when compared to otherpatient groups.

Optionally, the method comprises identifying clinical interventions thatare intended to modify the likelihood and/or consequences of the outcomeof interest for certain patient groups. The method may be applied withthe objective of decreasing the likelihood and/or consequences of anadverse outcome or to increase the likelihood and/or consequences of afavorable outcome.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram of a basic computer system that may be used toimplement a progression analytics system, according to aspects of thepresent disclosure;

FIG. 2 is a flow chart of an exemplary flow within a progressionanalytics system that provides development and analysis tools useful forretrospective processing of medical information with respect to outcomeof interest, according to aspects of the present invention;

FIG. 3 is a flow diagram illustrating the use of models and algorithmsin a progression analytic system to generate relevant electronicclinical data for use with the flow of FIG. 2, according to aspects ofthe present disclosure;

FIG. 4 is a flow diagram illustrating an approach to define patientgroups, for use with the flow of FIG. 2, according to aspects of thepresent disclosure;

FIG. 5 is a flow diagram illustrating an approach for definingetiological explanations that explain differences among patient groups,according to aspects of the present disclosure;

FIG. 6 is a flow diagram illustrating an approach for recommendingclinical interventions based upon etiological explanations fordifferences in corresponding patient groups, according to aspects of thepresent disclosure;

FIG. 7 is a flow chart of an exemplary flow within a progressionanalytics system that comprises defining patient groups, characterizingeach group with a common care element trajectory, translatingtrajectories into hypothesized physiological progressions, andidentifying opportunities to interrupt certain physiologicalprogressions according to aspects of the present disclosure, and

FIG. 8 is a block diagram of an exemplary computer system, which may beutilized for implementing one or more components of FIGS. 1-7, accordingto aspects of the present disclosure.

DETAILED DESCRIPTION

According to various aspects of the present disclosure, a progressionanalytics system is provided, which is utilized to extract electronicclinical data associated with historical healthcare encounters basedupon a patient-care related outcome of interest. The progressionanalytics system is further utilized to define patient groups based uponsimilar data patterns present in the extracted electronic clinical data.The patient groups are defined such that the patient groups may have avarying likelihood that the outcome of interest will occur, varyingconsequences associated with the outcome of interest, or both. Basedupon an analysis of the patient groups, hypothesized etiologicalexplanations are derived for explaining the differences among thepatient groups with regard to the outcome of interest. For instance, theetiological explanations may attempt to explain why one or more patientgroups have a different likelihood for the outcome of interest, why oneor more patient groups have a different consequence associated with theoutcome of interest, or both, when compared to other patient groups.These etiological explanations can be utilized to define interventionsand other treatment changes that may be included in patient careprotocols to improve patient care.

Clinical Progression:

The care seeking behavior of individuals most commonly begins when theyexperience signs and/or symptoms of an illness or experience some formor trauma. The clinical encounter begins when the individual ispresented to healthcare personnel. At the outset of the clinicalencounter, the individual is identified as a “patient” and a healthcarerecord is initiated. The individual's symptoms, which are usuallysubjective complaints, are recorded in the healthcare record. Signsobserved by the clinician are also recorded in the healthcare record.Both signs and symptoms may be indicative that a pathologic (disease)process is in progress. Conversely, both signs and symptoms mayrepresent normal physiologic processes and not be indicative of apathologic process.

Typically, the clinician(s) initiate a diagnostic process based upon thepatient's signs and symptoms. This diagnostic process often includes apatient evaluation, the nature of which depends upon the acuity(severity) of the illness/injury and the clinician's determinationwhether the initial signs and symptoms represent a potentially urgent oremergent patient need. From this early collection of information and aphysical examination, laboratory and other testing is typically orderedby the clinician in an effort to establish or rule/out variousdiagnoses.

A plan of action is implemented as indicated by the patient's clinicalcondition and in light of any early results available from the testsordered. At this point, the object of care is to establish and maintainnormal homeostatic physiologic functions. However, patient care is alsocarried out to correct any physiologic functions found to be abnormal,to identify pathologic processes causing or contributing to the abnormalphysiologic functions, to provide definitive care aimed at eliminatingthe culprit pathologic process restoring the individual to prior levelof health, etc.

All of the above information may be electronically captured during ahealthcare encounter associated with the patient, and representsexamples of ‘clinical data’. Moreover, the patient's experience goingthrough these various stages or steps is referred to herein as a‘clinical progression’. The continuation of underlying disease processis called the ‘pathologic progression’. The goal of treatment is to stopthe pathologic progression and restore normal homeostatic function atleast to the previous level of health prior to the illness/injury.

In the course of care, it is possible for a patient to experience afavorable outcome. It is also possible for a patient to experience anadverse outcome. Potential adverse outcomes may be subdivided into“active” and “latent.” ‘Active adverse outcomes’ can be characterized asknown potentially negative consequences or results that may occur due toan intended clinical intervention. Medications, surgical procedures,patient safety, infections, and childbirth provide well-known examples.‘Latent potential adverse outcomes’ are much less well understood. Alatent adverse outcome may occur unexpectedly even when the clinicalcare process is going well. For instance, latent adverse outcomes mayresult from an unexpected reaction to treatment, or a confluence ofuntoward effects. Latent adverse outcomes may also be attributed to“chance” because the nature of the occurrence is not known orunderstood.

According to various aspects of the present disclosure, systems, methodsand computer program products implement development and analysis toolsuseful for the retrospective processing of medical information, in amanner that facilitates the systematic analysis of the likelihood and/orconsequences of an outcome of interest based upon electronic clinicaldata. The outcome may be adverse or favorable. The analysis may bedirected to the likelihood of an occurrence of an outcome of interest.Alternatively, the analysis may be directed to the consequencesassociated with an outcome of interest (e.g., degree, severity,duration, etc.). Moreover, the above variations in analysis may beperformed in combination, e.g., by analyzing a combination of likelihoodand consequences, etc.

Platform Overview:

Referring now to the drawings and particularly to FIG. 1, a generaldiagram of a computer system 100 is illustrated, where components of thecomputer system 100 can be used to implement elements of a progressionanalytics system according to aspects of the present disclosure. In thisregard, the computer system 100 may be utilized to implement the methodsand processes described with reference to FIGS. 2-7 herein.

The computer system 100 can be deployed in a wide variety of manners,including within an outpatient office/clinic, hospital, integratedhealthcare network (IHN), within a location outside of where directpatient care is provided, etc. In this regard, the computer system 100or components thereof can be distributed across multiple differentlocations including multiple IHNs simultaneously. Moreover, computersystem components can be implemented by different entities with orwithout sharing data between the entities. Regardless of deploymentstrategy, the computer system 100 can be implemented by a source taskedwith identifying insights related to the occurrence of an outcome ofinterest, such as an adverse health outcome.

The computer system 100 comprises a plurality of processing devices,designated generally by the reference 102 that are linked together by anetwork 104. As will be described more fully herein, some processingdevices 102 of the computer system 100 are used for model and algorithmdevelopment, creation, maintenance, etc., whereas some processingdevices 102 are used in a corresponding clinical application, e.g., as auser interface utilized by treating clinicians or analysts to execute orotherwise implement the methods, processes and computer program productsdescribed herein.

As a few illustrative examples, the processing devices 102 can includeservers, personal computers and portable computers. As used herein,portable computers include a broad range of processing devices,including notebook computers, tablet computers, transactional systems,purpose-driven appliances (e.g., networkable medical machines), specialpurpose computing devices, personal data assistant (PDA) processors,cellular devices including smart telephones and/or other devices capableof communicating over the network 104.

The network 104 provides communications links between the variousprocessing devices 102, and may be supported by networking components106 that interconnect the processing devices 102, including for example,routers, hubs, firewalls, network interfaces, wired or wirelesscommunications links and corresponding interconnections, cellularstations and corresponding cellular conversion technologies, e.g., toconvert between cellular and tcp/ip, etc. Moreover, the network 104 maycomprise connections using one or more intranets, extranets, local areanetworks (LAN), wide area networks (WAN), wireless networks (WIFI), theInternet, including the World Wide Web, and/or other arrangements forenabling communication between the processing devices 102.

The illustrative progression analytics system 100 also includes a server108, which executes at least one processing engine 110 that interactswith at least one corresponding data source 112. The processingengine(s) 110 and data source(s) 112 may be used to support theprogression analytics system, e.g., by executing one or more aspects ofthe methods described with reference to FIGS. 2-7, as described ingreater detail herein. The results of the processing performed by theserver 108 can be communicated to the processing devices 102, e.g.,which may be stationed in hospital offices, at centralized locations, atremote locations, etc.

The flows, methods, processes, etc., described with reference any ofFIG. 2-FIG. 7 herein can be implemented on one or more of the systemcomponents of FIG. 1, e.g., the processing engine 110 executing on theserver 108. Moreover, the flows, methods, processes, etc., withreference to any of FIG. 2-FIG. 7 can be implemented as methods orcomputer program product code that is embodied on a computer readablestorage media. In this configuration, the code is executable by aprocessor to cause the processor to perform the corresponding methodsset out herein.

Progression Analytics:

Referring to FIG. 2, a computer-implemented method 200 is provided foridentifying insights related to the occurrence of a patient care-relatedoutcome of interest, e.g., an adverse health outcome. The method 200includes extracting, at 202, electronic clinical data associated withhistorical healthcare encounters for a plurality of patients. Here, theplurality of patients associated with the extracted electronic clinicaldata includes a first subset of patients that experienced the outcome ofinterest, and a second subset of patients that did not experience theoutcome of interest. An example method of extracting the electronicclinical data is described with regard to FIG. 3.

As used herein, ‘electronic clinical data’ is electronically stored datathat relates to healthcare encounters of individuals. Electronicclinical data may include electronic patient information such asdemographic data, patient medical historical data, physician practiceinformation, ambulance/emergency care information, laboratory results,triage results, measured vitals, electronic health records, etc.

Electronic clinical data may also include information that is utilizedby a progression analytics system implementing the method of FIG. 2. Forinstance, the electronic clinical data can include ‘likelihoodvariables’, ‘consequence variables’, an ‘outcome likelihood model’,outcome likelihoods that are computed for patients having a healthcareencounter included within the electronic clinical data, a ‘consequencelikelihood model’, outcome consequences that are computed for patientshaving a healthcare encounter included within the electronic clinicaldata, attributions of outcome likelihood to causal factors, combinationsthereof, etc.

As used herein, ‘likelihood variables’ are variables characterized asexpressions, functions or other extractions based upon electronicpatient information, which have a reconcilable relationship with anetiology of a corresponding outcome of interest, or which may begenerated based upon a computed statistical relationship for predictingan associated outcome of interest. In this regard, likelihood variablesmay be extracted directly from electronic patient information orlikelihood variables may be derived from electronic patient information.Moreover, the likelihood variables can be derived from datasets that arethe same as, or different from the electronic patient informationincluded in the electronic clinical data. In illustrativeimplementations, likelihood variables relate to the probability ofoccurrence of an outcome of interest.

As used herein, ‘likelihood factors’ are concepts that characterizefactors that are of interest in predicting the likelihood of aparticular outcome of interest (and for which a model is to be trained).In general, likelihood factors may be based upon outcome specificetiological knowledge such as causal relationships, conditions, origins,or reasons for an outcome specific condition.

As used herein, an ‘outcome likelihood model’ is a model that estimatesthe likelihood of the outcome of interest using a group of likelihoodvariables. In certain implementations, the outcome likelihood model maybe implemented as a ‘baseline and dynamic outcome likelihood model’. Inthis instance, each likelihood variable is classified into either abaseline group or a dynamic group. Here, the baseline group is composedof variables that hold a non-modifiable expression (e.g., constant valuefor a given patient healthcare encounter, such as date of admittance).Correspondingly, the dynamic group is composed of variables that canstore a modifiable expression (e.g., an expression having a value thatcan change over a given patient healthcare encounter, such as heartrate).

As used herein, an ‘outcome likelihood’ is a likelihood that aparticular outcome of interest will occur, which is determined using anoutcome likelihood model.

As used herein, ‘consequence variables’ are variables characterized asexpressions, functions or other extractions based upon electronicpatient information, which have a reconcilable relationship with anetiology of a corresponding outcome of interest, or which may begenerated based upon a computed statistical relationship for predictingthe consequences associated with an outcome of interest. In this regard,consequence variables are similar to likelihood variables, and may beextracted directly from electronic patient information or consequencevariables may be derived from electronic patient information. Moreover,the consequence variables can be derived from datasets that are the sameas, or different from the electronic patient information included in theelectronic clinical data. In an illustrative implementation, consequencevariables do not specifically relate to the probability of an occurrenceof on outcome of interest. Rather, consequence variables relate to otherfactors that follow or otherwise relate to consequences of the outcomeof interest, such as may be measured in time, duration, degree,severity, etc. Thus, an outcome of interest may occur across two or morepatients. However, that outcome may vary in numerous different measuresof impact, which may all be characterized by consequence.

As used herein, ‘consequence factors’ are concepts that characterizefactors that are of interest in predicting the consequences associatedwith a particular outcome of interest (and for which a model is to betrained). In general, consequence factors may be based upon outcomespecific etiological knowledge such as causal relationships, conditions,origins, or reasons for an outcome specific condition.

As used herein, an ‘outcome consequence model’ is a model that estimatesconsequences of the outcome of interest using a group of consequencevariables. In certain implementations, the outcome consequence model maybe implemented as a ‘baseline and dynamic outcome consequence model’ ina manner analogous to the baseline and dynamic likelihood model.

As used herein, an ‘outcome consequence’ is a consequence associatedwith a particular outcome of interest, which is determined using anoutcome consequence model.

As used herein, ‘attribution’ is information related to providinginsight as to those likelihood variables that are likely leading to thecomputed assessment of likelihood of the outcome of interest.

Further examples of defining the likelihood and consequence variables,an outcome likelihood model and attributions are set out in PCT Pat.App. No. PCT/US13/47189, to Haber et al., entitled “Clinical PredictiveAnalytics System” filed Jun. 21, 2013, the disclosure of which is hereinincorporated by reference in its entirety.

A feature of the method 200 that provides an inventive technicalcontribution includes defining, at 204, patient groups among theplurality of patients, where each patient group is defined by groupingtogether those patients having a similar data pattern present in theircorresponding extracted electronic clinical data.

Examples of data patterns are provided throughout. However, as a fewexamples, a patient group may be defined as including at least onepatient from the first subset of patients and at least one patient fromthe second subset of patients. In this manner, at least one patientgroup will include patient(s) that did experience the outcome ofinterest, and patent(s) that did not experience the outcome of interest.Moreover, the specific use of trajectories to define data patterns isdescribed with reference to FIG. 7.

For example, the method 200 may divide the plurality of patients up intopatient groups such that each patient belongs to only one patient group.Moreover, in an illustrative implementation, the data patterns areselected or otherwise generated such that the defined patient groupsdifferentiate from one another based upon the likelihood and/orconsequences of the outcome of interest. As a few illustrative examples,the data patterns may be defined to differentiate the groups based upona likelihood of an occurrence of the outcome of interest (includingcases where a group may have a likelihood of 0). As another example, thedata patterns may be utilized to differentiate the groups based upon avarying consequence in an outcome of interest. As an illustration, anadverse outcome may be unavoidable. However, groups may have experienceda different consequence of the outcome of interest, e.g., in terms ofdegree, severity, duration, etc. An illustrative method of definingpatient groups is described with regard to FIG. 4. Moreover, an examplemethod of deriving the data patterns from the extracted electronicclinical data, and grouping together those patients having a similardata pattern, is described with reference to FIG. 4.

Yet another feature of the method 200 that provides an inventivetechnical contribution includes deriving, at 206, hypothesizedetiological explanations for why one or more patient groups havevariations in the likelihood and/or consequences of the outcome ofinterest when compared to other patient groups. As noted in greaterdetail herein, the explanations may attempt to explain likelihood ofoccurrence, consequence, combinations thereof (such as risk), etc. Anexample method of defining hypothesized etiological explanations isdescribed with regard to FIG. 5.

The method 200 can be implemented in a manner such that the patientcare-related outcome of interest is selected as a favorable healthoutcome. In this regard, hypothesized etiological explanations mayattempt to explain why one or more patient groups experienced anincrease in the likelihood and/or favorable consequences of thefavorable health outcome relative to other patient groups.

As another example, the method 200 can alternatively be implemented in amanner such that the patient care-related outcome of interest isselected as an adverse health outcome. In this regard, hypothesizedetiological explanations may attempt to explain why one or more patientgroups have a different risk (e.g., likelihood of occurrence,consequence, or a combination thereof) associated with the outcome ofinterest, relative to other patient groups. The etiological explanationsmay attempt to explain the reasons that decrease the risk, e.g.,likelihood and/or negative consequences, of a select patient groupattaining the adverse health outcome, regardless of whether a particularadverse outcome instance was active or latent.

As an optional process, the method 200 may also include identifying, at208, clinical interventions that have the potential to impact thelikelihood and/or consequences of an outcome of interest, e.g., to lowerthe likelihood of the adverse health outcome. An example method ofidentifying clinical interventions is described with regard to FIG. 6.

Clinical Data Extraction:

Referring to FIG. 3, a computer-implemented component 300 of aprogression analytics system is provided for identifying electronicclinical data, e.g., for extracting at 202 of FIG. 2, for definingpatient groups at 204 of FIG. 2 etc.

In the exemplary component 300, electronic patient data is provided in adata source 302. As noted in greater detail herein, the electronicpatient data can include information collected as a result of patienthealthcare encounters, such as demographic data, patient medicalhistorical data, physician practice information, ambulance/emergencycare information, laboratory results, triage results, measured real timevitals, electronic health records, patient medical history, etc.

The electronic patient data may be electronically stored in the datasource 302 as structured or unstructured data, in a proprietary formator otherwise. Also, the electronic patient data may include historicalpatient data to be analyzed, including patient data for healthcareencounters related to an outcome of interest, patient data forhealthcare encounters not related to an outcome of interest orcombinations thereof. As such, it may be necessary to prune theavailable data in the data source 302 to generate electronic clinicaldata that is determined to be statistically relevant to the analysis ofthe outcome of interest.

An optional data abstraction process 304 receives as input, theelectronic patient data from the data source 302, which may be inproprietary format(s) and converts, transforms, etc., (i.e., maps) theproprietary data to a standardized generic format, schematicallyrepresented by the data source 306. Moreover, the conversion of patientdata to a standardized format is optional and may not be necessary,e.g., where the patient data is already available in a data formatsuitable for processing.

Likelihood Variables:

Likelihood variables 308 that are determined to be relevant to thelikelihood of the outcome of interest, are extracted from, computedfrom, or otherwise derived from (i.e., mapped from) the electronicpatient data 306 (if the likelihood variables 308 are not otherwiseavailable from another source, e.g., pre-computed). As noted above, thelikelihood variables may be generated so as to have a reconcilablerelationship with an etiology of the outcome of interest. As anotherexample, likelihood variables may be generated based upon a computedstatistical relationship for predicting the occurrence of the outcome ofinterest.

In an illustrative example, a system user such as a healthcare dataanalyst or a clinical subject matter expert interacts with the component300 of the progression analytic system through a graphical userinterface (GUI) to perform a likelihood variable selection process.Briefly, in an exemplary approach, outcome specific etiologicalknowledge is transformed into likelihood factors, and those likelihoodfactors are reconciled into likelihood variables.

In this example, the system user utilizes outcome specific etiologicalknowledge to identify likelihood factors, e.g., concepts thatcharacterize factors that are of interest in predicting the likelihoodof the particular outcome of interest (and for which a model is to betrained). In general, the outcome specific etiological knowledge mayinclude causal relationships among likelihood factors, conditions,origins, or reasons for an outcome specific condition.

The system user interacts with the component 300 of the progressionanalytics system through the GUI to construct likelihood variables 308that reconcile with the identified likelihood factors. Here, thelikelihood variables 308 may be calculated, derived, transformed, mappedor otherwise obtained from the electronic patient data in the datasource 306.

Likelihood variables may also be generated using the methods andtechniques set out in PCT Pat. App. No. PCT/US13/47189, to Haber et al.,entitled “Clinical Predictive Analytics System” filed Jun. 21, 2013, thedisclosure of which is already incorporated by reference in itsentirety.

Outcome Likelihoods:

The system user may further interact with component 300 of theprogression analytics system through the GUI to construct an outcomelikelihood model 312.

As an illustrative example, an outcome likelihood model form andvariable selection process is guided by a training data set (e.g., asubset of data within the electronic patient data 306) that includesboth outcome data and non-outcome data. In an illustrative example, theoutcome likelihood model form and variable selection process outputs amodel that takes the general form: Y=log(P/(1−P))=β₀+β₁x₁+ . . .+β_(k)x_(k). In this example implementation, estimated outcomelikelihoods are computed based upon logistic regression models, thuspredicting the likelihood that a person will experience an outcome ofinterest in the near future, e.g., during the healthcare encounter. Inthis example, there are k likelihood variables where β₁-β_(k) representmodel coefficients. In practice, the training data set is used to fitthe model. The model then determines if β should be adjusted up or down,whether factor x_(i) should be dropped, etc. The model itself determineswhich parameters are important.

In an exemplary configuration, the likelihood variables 308 are dividedinto baseline likelihood variables and dynamic likelihood variables.Variables deemed to be non-modifiable based on the medical care that isprovided to the patient are classified as baseline likelihood variables(also referred to generally as baseline variables). Correspondingly,variables affected by the medical care that the patient receives whilein the hospital will be classified as dynamic likelihood variables (alsoreferred to generally as dynamic variables). The baseline and dynamiclikelihood variables and the training set are utilized to generatebaseline and dynamic likelihood model forms at 312.

Baseline and dynamic outcome likelihood models may also be generated foruse at 312 using the methods and techniques set out in PCT Pat. App. No.PCT/US13/47189, to Haber et al., entitled “Clinical Predictive AnalyticsSystem” filed Jun. 21, 2013, the disclosure of which is alreadyincorporated by reference in its entirety.

Clinical Data Selection:

As noted in greater detail herein, the progression analytic systemextracts clinical data associated with historical healthcare encountersfor patients that are selected for the evaluation of an outcome ofinterest. In this regard, a clinical data selection process 314 isutilized to select the clinical data that will be utilized for theanalysis. In an illustrative example, a system user interacts with thecomponent 300 of the progression analytics system through a GUI toestablish inclusion criteria to filter the data in the data source 306to selectively extract patients having relevant historical healthcareencounter data (which include patients that experienced the outcome ofinterest and patients that did not experience the outcome of interest).

As an illustrative example, the clinical data selection process 314interacts with the data source 306 to input patient healthcareencounters into an outcome likelihood calculation process 316, whichuses the likelihood variables 308 and the outcome likelihood model(s)312 to compute outcome likelihoods 318 for the selected healthcareencounters. The clinical data selection process 314 applies inclusionand/or exclusion criteria to filter the healthcare encounter data suchthat only selected patient data is organized as the set of clinical datastored at 322.

To ensure the selection of meaningful patient healthcare encounters, theclinical data selection process 314 may utilize the inclusion/exclusioncriteria to place restrictions on the likelihood variable values for apatient healthcare encounter under consideration. As further examples,the inclusion/exclusion criteria may be evaluated against the results ofthe outcome likelihood computations at 318. As yet a further example,the inclusion/exclusion criteria can take other factors intoconsideration, such as demographics, etc.

In certain illustrative implementations, the clinical data selectionprocess 314 may further utilize an attribution process 320 to provideinformation as to which likelihood variables are driving the computedoutcome likelihoods 318. For example, the attribution process 320 maycharacterize the degree to which likelihood for the outcome of interestcan be attributed to individual likelihood variables or collections oflikelihood variables. This information can be processed through theinclusion/exclusion criteria to determine if the patient healthcareencounter should be selected into the clinical data 322.

As noted in greater detail herein, the selected clinical data stored at322 can also include the likelihood variables 308, the computed outcomelikelihoods 318 and other data. Moreover, in certain illustrativeimplementations, the selected electronic clinical data can includetrajectory data. Trajectory data is electronic clinical data thatincludes information that is analyzed for generating care elementtrajectories as will be described in greater detail, with reference toFIG. 7.

The selected electronic clinical data 322 may be utilized as theelectronic clinical data extracted at 202 of FIG. 2. However, othertechniques and approaches may alternatively be implemented to select theelectronic clinical data utilized by the method 200 of FIG. 2.

In alternative configurations, it may be more desirable to measureconsequence rather than likelihood. Here, the component 300 of aprogression analytics system may be provided, where the likelihoodvariables, likelihood factors, outcome likelihood, etc. are replacedwith consequence variables, consequence factors, outcome consequences,etc.

Group Identification:

Referring to FIG. 4, a computer-implemented component 400 of aprogression analytics system is provided to define patient groups, e.g.,at 204 of FIG. 2.

The component 400 utilizes selected electronic clinical data in datasource 402. In an exemplary implementation, the information in the datasource 402 is the electronic clinical data stored in the data source 322(i.e., selected electronic clinical data) described with reference toFIG. 3. Alternatively, the data source 402 may comprise selectedelectronic clinical data that was selected using alternative approaches.

As noted in greater detail herein, patient groups are defined bygrouping together those patients having ‘similar data patterns’.However, a patient may generate a significantly large amount of dataduring a healthcare encounter. As such, to maintain manageablepopulations of groups, the data pattern describing each group can bebased upon a finite number of measures. Moreover, a “pattern” defining agroup can be based upon measures that are defined in terms of a specificdata value, ranges of values, transitions over time, or any otherdesired manner to designate the requirements for membership to aparticular group. Still further, data patterns may be determined basedupon trajectory, as described in greater detail with reference to FIG.7.

It is likely that not all of the patient data will contributesignificantly to the outcome of interest. As such, the component 400 canuse likelihood variables 404 for consideration of the selection of thegroups. The likelihood variables at 404 may be the likelihood variables308 described with reference to FIG. 3, or a subset thereof.Alternatively, the likelihood variables at 404 can be derived from thepatient data, e.g., using techniques as set out more fully herein, orusing other techniques.

The component 400 employs a process at 406 that contributes to definingone or more patient groups (e.g., as set out in 204 of FIG. 2). In thisregard, the process at 406 processes one or more discrete likelihoodvariables. Here, ‘discrete’ likelihood variables refers to a subset ofthe likelihood variables 404 for which a likelihood variable takes onone of a finite set of discrete values, or where a measure can bedefined that expresses a discrete representation of a correspondinglikelihood variable 404 (or group of likelihood variables 404). In thisregard, the component 400 generates at 408, patient group(s) with fixedlikelihood variable values. The component 400 may also generate at 410,fixed likelihood variable values by patient group.

By way of example, a likelihood variable may have one of a limitednumber of values, e.g., a binary value. In other instances, a likelihoodvariable value is compared to a threshold or range of thresholds totransform the value of the likelihood variable into a discrete value. Inillustrative examples, a system user can interact with the progressionanalytics system through a GUI to set the thresholds, adjust thethresholds or otherwise manipulate the process 406 processingparameters.

By way of example, the system user may utilize the GUI of the component400 to select a subset of likelihood variables 404, then transform thevalues for patient healthcare encounters corresponding to that subset oflikelihood variables 404 into discrete values (e.g., binary, or othernumber of discrete values) based upon thresholds, rules, algorithms,etc.

In many cases, the process 406 is sufficient to generate the patientgroups necessary for further evaluation. However, in certain cases,there are likelihood variables of interest that have analog values,values that change over time, or are otherwise difficult to transforminto discrete representations (measures). As such, the component 400also (or alternatively to the process 406) implements a process 412 thatalso contributes to defining patient groups (e.g., as set out in 204 ofFIG. 2). For instance, the process 412 may cluster or otherwise segmentthe extracted electronic clinical data and define a patient group foreach data cluster or segment. For example, the process 412 may accept apatient into a particular group based upon a computation that places theclinical data associated with the healthcare encounter for that patientwithin a user-defined range of a centroid in a cluster.

In the context of FIG. 4, the process at 412 clusters or segments thepatient healthcare encounter data to define patient groups. The outputof the process at 412 is a defined set of patient groups 414, whereinthe patient groups may have varying values for one or more of thelikelihood variables. The patient groups and corresponding likelihoodvariable data at 414 are provided to a process at 416 that defines“typical” likelihood variable values for each patient group. The outputof the process at 416 is data at 418 that defines “typical” likelihoodvariable values by patient group.

In illustrative examples, the system user can interact with thecomponent 400 of the progression analytics system through a GUI to setthe clustering algorithms, adjust the thresholds, centroid range, orotherwise manipulate the process 412 processing parameters.

In yet further implementations, the patient groups may be defined basedupon a combination of discrete likelihood variable representations andclustered (e.g., analog) likelihood variables. As such, the set ofpossible groups may be defined solely by the process 406, solely by theprocess 412, or by a combination of the process 406 and process 412,depending upon the nature of the likelihood variables 404. Further, thedata patterns employed to define patient groups may include definingvalues of one or more static variables that do not change during thecourse of a hospital encounter and/or defining a pattern across a timehistory of changes in the physiological state of patients, occurrencesof events that the patients experience, or both.

The component 400 also includes an outcome likelihood model at 420,which receives as input, data from the data source 402. The outcomelikelihood model at 420 may be the outcome likelihood model 312described with reference to FIG. 3, or a model that is constructed usingother techniques. For instance, the outcome likelihood model 420 may bedeveloped as an outcome likelihood scoring algorithm that characterizesthe likelihood of a specific outcome as a function of variables derivedfrom the data stored in the data source 402, e.g., the extractedclinical data. This facilitates a process at 422 having the ability todefine patient groups based on data patterns in the variables employedin a scoring algorithm. In yet another alternative configuration, theoutcome likelihood model may be implemented as the calculation of actuallikelihood for historical patients belonging to the patient groups.

The component 400 also includes a process 422 that calculates apredicted outcome likelihood by patient group, using as inputs, thefixed likelihood variable values by patient group at 410, and/or the“typical” likelihood variable values by patient group at 418, and theoutcome likelihood model at 420. The output of the process at 422 isdata at 424 that represents patient groups with predicted outcomelikelihood. As such, the process of FIG. 4 defines patient groups amongthe plurality of patients, where each patient group is defined bygrouping together those patients having a similar data pattern presentin the extracted electronic clinical data, where the defined patientgroups have varying likelihood for the adverse health outcome ofinterest.

In further exemplary implementations, the process 422 may compute orotherwise receive as input, a calculation representing the actualpercentage of patient healthcare encounters in each group thatexperienced the outcome of interest. As such, the process 422 cancompare the actual percentage of patient healthcare encounters in eachgroup that experienced the outcome of interest with the correspondingcomputed likelihood of an occurrence of the outcome of interest. Thiscomparison can be utilized to provide confidence in the selection of therelevant parameters. For instance, a strong correlation between thecomputed likelihoods and actual percentages provides confidence that theanalysis will provide meaningful results. If the computed likelihoodsand actual percentages do not correlate, then there could be an issuewith the selection of likelihood variables 404, with the model form ofthe outcome likelihood model 420, with the thresholds or otherparameters utilized by the system user to process the patient healthcareencounters, etc. This provides an opportunity for feedback to makeadjustments to the previous processes and methods.

As noted in greater detail, a patient group may be defined as includingat least one patient from the first subset of patients and at least onepatient from the second subset of patients. In this manner, at least onepatient group will include patient(s) that did experience the outcome ofinterest, and patent(s) that did not experience the outcome of interest.

Moreover, as noted above, patients having a similar data pattern presentin their corresponding extracted electronic clinical data may be groupedby defining similar data patterns based upon the data values of a subsetof the likelihood variables, consequence variables or both, such thatthe patient groups are defined in terms of variables and not in terms ofwhether or not the patient has experienced the outcome of interest.Moreover, the subset of the likelihood variables, consequence variablesor both, may be converted into discrete measures having a fixed numberof value options. Thus, methods herein may group together those patientshaving the same data values associated with the discrete measures.

According to various aspects of the present disclosure, the organizationof the patient groups results in groups having different likelihoods ofthe outcome of interest. In alternative configurations, it may be moredesirable to measure consequence rather than likelihood. Here, thecomponent 400 of a progression analytics system may be provided, wherethe likelihood variables, discrete likelihood variables, outcomelikelihood model, predicted outcome likelihood, etc., are replaced withconsequence variables, discrete consequence variables, outcomeconsequence model, predicted outcome consequence, etc.

Hypothesized Etiological Explanations:

According to aspects of the present invention, the progression analyticssystem provides a GUI that enables a system user to derive hypothesizedetiological explanations that correlate with comparisons of variouspatient groups.

Referring to FIG. 5, a component 500 is provided for selecting one ormore collections of patient groups to derive hypothesized etiologicalexplanations for the likelihood differences among the patient groups ineach collection. For example, explaining why one patient group in acollection has a higher likelihood of the outcome of interest thananother patient group in the collection. For example, in an exemplaryimplementation, the component 500 or other interface associated with theprogression analytics system presents to the system user a series ofpatient group pairs. For each pair, the system reports the difference inoutcome likelihood between the two groups in each pair and reports thelikelihood variables that are most different for the two groups in eachpair, e.g., the distinguishing likelihood variables. The system user maythen interact with the reported information to first select patientgroup pairs with large likelihood differences and then select patientgroup pairs for which the distinguishing likelihood variables derivefrom likelihood factors that have the potential to be controllable viapatient care protocols.

The component 500 starts with data at 502 that defines patient groupswith predicted outcome likelihood. For instance, the data at 502 maycomprise the data at 424 that is generated by the component 400described with reference to FIG. 4. A process at 504 selects collectionsof patient groups for which the likelihood differences among the groupsare potentially explainable by controllable likelihood factors.

This selection process may be implemented by a system user interactingwith the progression analytics system though a GUI. The process at 504,which can select collections of patient groups for which the likelihooddifferences among the groups are potentially explainable by thecontrollable likelihood factors, outputs data at 506 that definesselected collections of patient groups. As illustrated, the process at504 may include inputs from other sources, including data at 508corresponding to clinical subject matter expertise, data at 510corresponding to data extracted from a clinical knowledgebase,combinations thereof, etc. A process at 512 receives as input, the dataat 506 representing the selected collections of patient groups. Theprocess at 512 may also include inputs from other sources, includingdata at 508 corresponding to clinical subject matter expertise, data at510 corresponding to data extracted from a clinical knowledgebase,combinations thereof, etc.

The clinical knowledgebase 510 is an information repository thatprovides a means for clinical information to be collected, organized,shared, searched and utilized. In this manner, the clinicalknowledgebase 510 represents a knowledge repository that is organized byphysiological condition or adverse outcome and captures knowledge aboutoperating in the clinical setting. An example of approaches forconstructing the clinical knowledgebase 510 is described more fully inPCT Pat. App. No. PCT/US13/47189, to Haber et al., entitled “ClinicalPredictive Analytics System” filed Jun. 21, 2013, the disclosure ofwhich is already incorporated by reference in its entirety.

The process at 512 derives hypothesized etiological explanations for thelikelihood differences among the patient groups in each collection. Forexample, to develop an etiological explanation for the likelihooddifference between a pair of patient groups, the system user firstobtains the distinguishing likelihood variables from the system (e.g.,as described above with reference to 504) and identifies the likelihoodfactors from which the distinguishing likelihood variables derive. Thesystem user may then access clinical subject matter expertise and otherclinical knowledge (e.g., see 508, 510) to relate the differences indistinguishing likelihood variable values to physiological processesassociated with the likelihood factors and the outcome of interest. Theresulting explanation would identify hypothesized physiologicalprocesses that are responsible for the difference in outcome likelihoodbetween the two patient groups.

Thus, for example, the process at 512 may derive hypothesizedetiological explanations for why one or more patient groups have higherlikelihood of an adverse outcome of interest when compared to otherdefined patient groups. The output of the process at 512 is data at 514that defines selected patient group collections with etiologicalexplanations for likelihood differences within each collection.

Referring to FIG. 4 and FIG. 5 generally, the patient groups andhypothesized etiological explanations may be defined for the specificpurpose of identifying clinical interventions that have the potential tolower the likelihood of the adverse health outcome of interest orincrease the likelihood of a favorable health outcome of interest.

In alternative configurations, it may be more desirable to measureconsequence rather than likelihood. Here, the component 500 of aprogression analytics system may be provided, where the predictedoutcome likelihoods, likelihood differences, likelihood factors etc.,are replaced with predicted outcome consequence, consequencedifferences, consequence factors etc.

Clinical Interventions:

According to aspects of the present invention, the progression analyticssystem provides a GUI that enables a system user to identify clinicalinterventions intended to modify or prevent select physiologicalprocesses.

Referring to FIG. 6, a component 600 is provided for identifyingclinical interventions. The identified clinical interventions may bebased upon previously derived hypothesized etiological explanations forwhy one or more patient groups have different likelihood of the outcomeof interest when compared to other defined patient groups. For instance,the hypothesized etiological explanations may comprise physiologicalprocess descriptions that hypothesize a causal relationship between thedata patterns that define patient groups and the likelihood ofoccurrence of the outcome of interest for individual groups. Moreover,as noted with regard to 508 and 510, the hypothesized etiologicalexplanations may be derived, at least in part, based on clinical subjectmatter expertise, based on the contents of a clinical knowledgebase, orbased on a combination of the two.

The method at 600 starts with data at 602 that defines selected patientgroup collections with etiological explanations for likelihooddifferences within each collection. For instance, the data at 602 maycomprise the data at 514 that is generated by the component 500described with reference to FIG. 5. A process at 604 selectsphysiological processes within the etiological explanations that arepotentially modifiable to change the likelihood of the outcome ofinterest.

For example, in an exemplary implementation, the component 600 or otherinterface associated with the progression analytics system presents tothe user a series of patient group pairs along with hypothesizedetiological explanations for the outcome likelihood differences betweenthe groups in each pair where the hypothesized etiological explanationsinclude hypothesized physiological processes that are responsible forthe difference in outcome likelihood. The system user may then accessclinical subject matter expertise and other clinical knowledge (see 608,610) to identify the physiological processes having the greatestpotential to be modified in a manner that either decreases thelikelihood of an adverse outcome or increases the likelihood of afavorable outcome.

The process at 604 outputs data at 606 that represents selectedphysiological processes associated with specific patient groups. Asillustrated, the process at 604 may include inputs from other sources,including data at 608 corresponding to clinical subject matterexpertise, data at 610 corresponding to data extracted from a clinicalknowledgebase, combinations thereof, etc. The clinical knowledgebase 610is an information repository such as the clinical knowledgebase 510described with reference to FIG. 5. A process at 612 receives as input,the data at 606 representing the selected physiological processes. Theprocess at 612 may also include inputs from other sources, includingdata at 608 corresponding to clinical subject matter expertise, data at610 corresponding to data extracted from a clinical knowledgebase,combinations thereof, etc. The process at 612 identifies clinicalinterventions intended to modify or prevent the selected physiologicalprocesses. For example, based on a list of physiological processes forwhich modification has the potential to improve patient outcomes, thesystem user could access clinical subject matter expertise and otherclinical knowledge to identify clinical interventions, interventionsthat could be implemented in the clinical setting, directed to modifythe physiological processes with a result that either decreases thelikelihood of an adverse outcome or increases the likelihood of afavorable outcome. The output of the process at 612 is data at 614 thatdefines recommended clinical interventions for specific patient groups.

If the outcome of interest is an adverse outcome, the recommendations ofclinical interventions for one or more patient groups may be intended todecrease the likelihood of the adverse health outcome, reduce theconsequences of the adverse health outcome, or both. In this regard, theclinical interventions may be identified that are directed to preventpatients from entering higher-likelihood patient groups, preventpatients from entering higher-consequence patient groups, lower thelikelihood of the adverse outcome for patients in higher-likelihoodpatient groups, lower the consequences of the adverse outcome forpatients in higher-consequence patient groups, or any combination of theforegoing, etc.

If the outcome of interest is a favorable outcome, the recommendationsof clinical interventions for one or more patient groups may be intendedto increase the likelihood of the favorable health outcome, increase theconsequences of the favorable health outcome, or both. In this regard,the clinical interventions may be intended to assist patients inentering higher-likelihood patient groups, assist patients in enteringhigher-consequence patient groups, increase the likelihood of thefavorable outcome for patients in lower-likelihood patient groups,increase the consequences of the favorable outcome for patients inlower-consequence patient groups, or any combination of the foregoing,etc.

As noted with regard to 608, 610, the clinical interventions may beidentified based on clinical subject matter expertise, based on thecontents of a clinical knowledgebase, or based on a combination of thetwo.

In alternative configurations, it may be more desirable to measureconsequence rather than likelihood. Here, the component 600 of aprogression analytics system may be provided, where the likelihooddifferences etc., are replaced with consequence differences, etc. Inanother example, where the measure of interest is risk, the likelihooddifferences, etc., are replaced with risk differences, etc. Othermeasures may also be utilized, e.g., which integrate, blend or otherwisestrike a balance between likelihood and consequence.

Simplified Example to Explain Certain Concepts Herein

As noted in greater detail herein, insights into a patient-care relatedoutcome of interest are identified by extracting electronic clinicaldata associated with historical healthcare encounters for a plurality ofpatients, where the plurality of patients are selected for theevaluation of an outcome of interest.

Take as an example, an adverse outcome of interest such as acute kidneyinjury (AKI). The systems and methods herein are utilized to identifylikelihood variables (e.g., 308, 404) that have a clinical and/orstatistical significance to AKI. Assume for this simplified example thatthree likelihood variables are identified, such as urinary output rate,respiratory rate, and serum creatinine concentration.

The method then defines patient groups among the plurality of patients,where each patient group is defined by grouping together those patientshaving a similar data pattern present in the extracted electronicclinical data. To implement this, the method first defines “datapatterns”. Here, the nature of the likelihood variables, the precisionat which the likelihood of an adverse outcome can be computed, and otherlike considerations will decide how the data patterns are defined. Forinstance, in the present example, a simplified data pattern is definedby transforming the likelihood variables into discrete measures. Themeasures may be utilized to define one or more states for the likelihoodvariables, e.g., by defining ranges, groupings, orders, etc. Forinstance, in the simplified example, each measure represents a binaryexpression of a corresponding likelihood variable.

By way of example, the likelihood variable “urinary output rate” istransformed into a “high urinary output rate” measure, represented as abinary. If a particular patient is classified as having a high urinaryoutput rate (e.g., by comparing the value corresponding to the patient'surinary output rate to a threshold), the high urinary output measure isYes, represented by a data value 1.

Analogously, the likelihood variable “respiratory rate” is transformedinto a “high respiratory rate” measure, represented as a binary. If aparticular patient is classified as having a high respiratory rate(e.g., by comparing the value corresponding to the patient's respiratoryrate to a threshold), the high respiratory rate measure is Yes,represented by a data value 1.

Likewise, the likelihood variable “serum creatinine concentration” istransformed into a “low serum creatinine concentration”, represented asa binary. If a particular patient is classified as having a low serumcreatinine concentration (e.g., by comparing the value corresponding tothe patient's serum creatinine concentration to a threshold), the lowserum creatinine concentration measure is Yes, represented by a datavalue 1.

Notably, this approach simplifies the analysis considerably, because 3binary values can create up to eight groups. For each unique group, themethod predicts the outcome likelihood as described in greater detailherein, producing predicted outcome likelihoods for each group.

As noted in greater detail herein, since the data used to select thepatient groups is retrospective data, the patient data for the membersof each group may indicate whether the patient did, or did not actuallysuffer the adverse outcome of interest, AKI in this example. As such,the method has an opportunity to compare the predicted outcomelikelihood for each group with the actual likelihood of the adverseoutcome for that group. Where such information is available, the methodmay compare the predicted likelihood value with the computed actuallikelihood value for each group. If there is a strong correlationbetween the predicted outcome likelihood and the computed actual outcomelikelihood, then there is a strong confidence in the outcome likelihoodmodel. If the comparison of the predicted outcome likelihood to thecomputed actual outcome likelihood is too unfavorable, the method mayiterate back, e.g., to select new likelihood variables, to alter thedefinitions of the measures, e.g., to be more granular, etc.

Assuming that the predicted outcome likelihood correlates well to anactual outcome likelihood for each group, the method next focuses ondifferences between groups, e.g., based upon their predicted outcomelikelihood. For instance, keeping with the above-example, assume thatthe following computations are realized:

Patient group A has a high urinary output rate measure of Yes, a highrespiratory rate measure of No and a low serum creatinine concentrationmeasure of Yes, with a calculated predicted outcome likelihood of 3%.

Patient group B has a high urinary output rate measure of Yes, a highrespiratory rate measure of Yes and a low serum creatinine concentrationmeasure of Yes, with a calculated predicted outcome likelihood of 31%.

Comparing Patient group A to Patient group B, the only differencebetween them is the High respiratory rate. However, the predictedoutcome likelihood for Group B is an order of magnitude higher than thepredicted outcome likelihood for group A. As such, the method deriveshypothesized etiological explanations for why a member of Patient groupB has a higher likelihood of the adverse outcome of interest whencompared to a member of Patient group A by focusing on the Highrespiratory rate of the patients in the Patient group B. For instance, afocused evaluation can attempt to ascertain one or more physiologicalprocesses that potentially explain why the patients in Group B with highrespiratory rates have a much higher likelihood of AKI. By understandingthe physiological processes leading to AKI, the method can identifyclinical interventions for patient Group B (type patients) where theinterventions are intended to decrease the likelihood of AKI, reduce theconsequences of AKI or both by preventing future patients that wouldotherwise be classified in patient Group B from developing a highrespiratory rate or preventing patients in patient group B fromexperiencing AKI.

The above example is for illustration and clarity of explanation of theconcepts herein. In practice, there may be more than three likelihoodvariables associated with an outcome of interest. Moreover, eachlikelihood variable need not be expressed as a measure that is binary.Rather, the resolution of each likelihood variable may be determinedbased upon the number of variations in actual value of patients in thegroups. Still further, one or more of the likelihood variables may berepresented by a complex structure, such as a dynamic definition, onethat includes temporal changes, and interaction among likelihoodvariables, a temporal ordering of events, etc.

Progression Identification Using Trajectories:

Referring to FIG. 7, a method is provided for identifying physiologicalprogressions, according to aspects of the present disclosure. The methodcomprises extracting, at 702, electronic clinical data associated withpatients under evaluation. The extraction at 702 is analogous to theextraction at 202 described with reference to FIG. 2. As such, thediscussion with regard to extracting electronic clinical data, asdescribed with reference to the preceding figures, may be utilized forthe extraction at 702.

The method comprises defining at 704, patient groups. The defining ofpatient groups at 704 is largely analogous to defining patent groups at204 described with reference to FIG. 2. As such, the discussion withregard to defining patient groups, as described with reference to thepreceding figures, may be utilized for defining the patient groups at704. However, defining patient groups at 704 also includes generatingpatient groups based upon similar temporal, static, etc., data patternsextracted from the electronic clinical data.

In an illustrative example, a data pattern may be employed to definepatient groups that comprise defining a pattern across a time history ofchanges in the physiological state of patients, occurrences of eventsthat the patients experience, or both. In as yet another example, thevariables that are used to define the data patterns need not be alltemporal. Rather, in illustrative implementations, the data patternsemployed to define patient groups include both non-temporal and temporaldata patterns.

In this regard, the method 700 also comprises identifying a commontrajectory associated with at least one defined patient group at 706where the trajectories represent values of static variables and datapatterns across a time history of changes in the physiological state ofpatients, occurrences of events that patients experience, or acombination of states and events over time. For instance, the extractedelectronic clinical data and the constructed (likelihood, consequence)model can be utilized with data processing techniques that stratify andsegment or cluster patients into patient groups that may becharacterized by care element trajectories. These care elementtrajectories represent data patterns across a time-limited history ofchanges in the physiological state of patients, occurrences of eventsthat patients experience, or a combination of physiological states andevents over time.

Also, the method 700 comprises translating the common trajectory to aphysiological progression at 708 where the physiological progression isa time-sequenced set of physiological processes that provide ahypothetical etiological explanation for why members of the patientgroup may experience an outcome of interest. The method 700 furthercomprises identifying opportunities to interrupt one or more of thephysiological processes involved in one or more of the physiologicalprogressions for the purpose of reducing the likelihood of an adverseoutcome, mitigating the consequences of an adverse outcome, or both.

Thus, progressions are constructed that characterize sequences ofconditions that patients progress through on a path to an outcome(either favorable or unfavorable). The progressions are constructedusing common trajectories defined by patient groups, where the patientgroups are generated based upon similar (temporal, non-temporal, or acombination thereof) data patterns, of patients within the group.

Moreover, a trajectory is identified, which is associated with at leastone defined patient group. For instance, the patient groups can beidentified by analyzing the electronic clinical data over time andclustering or otherwise segmenting the electronic clinical data. Theelectronic clinical data is clustered or segmented so as to definegroups of patients that have similar data patterns. From the clusters orsegments of data associated with the patient groups, trajectories can beextracted. A trajectory represents a data pattern and can be mapped toan implied condition pattern. The implied condition pattern maycorrespond to a hypothesized physiological progression. Thus, thetrajectory that a patient (or patient group) follows, may be mapped to aphysiological progression.

In this regard, more than one trajectory may be derived. Also, atrajectory may be determined based upon a statistical analysis of theclustered or segmented data points representing the patients belongingto a group. For instance, the trajectory may pass through the centroidof the patients belonging to a particular group.

Aspects of the present disclosure present an opportunity to compare andotherwise evaluate trajectories, and to correlate trajectories withphysiological progressions. The identification of a physiologicalprogression presents an opportunity to intervene and alter theprogression using interventions that have been established as capable oflowering the likelihood of an adverse outcome.

As another illustrative example, the patterns can be used as a tool tomitigate the likelihood that a patient remains on a trajectory thatultimately leads to an adverse outcome. By analyzing the physiologicalprogressions, a clinician can identify opportunities to adjust patientcare, e.g., by amending patient care protocols, etc.

As an example, consider acute kidney injury (AKI). After analyzing theelectronic clinical data as set out above, the data may suggest thatthere are six ways (six trajectories that each identify a correspondingphysiological progression) that can progress to AKI. It may turn outthat two of the six identified physiological progressions can beeliminated with a systematic protocol change for the treatment ofpatients. It may further turn out that the likelihood of AKI along twoof the six identified physiological progressions can be greatly reducedwith a systematic protocol change for the treatment of patients. It maystill further turn out that two of the six identified physiologicalprogressions cannot be affected. In this illustrative, but non-limitingexample, overall patient care is improved through the systematic changesin care protocols learned through the retrospective analysis ofelectronic clinical data.

Moreover, the systematic protocol changes, e.g., new, modified oreliminated intervention that is being considered by a clinician can beverified for its effectiveness in certain circumstances. For instance,by changing a protocol for care, the system user changes the trajectorythat patient is following. As such, the system user can go back to theclustered or otherwise segmented data and identify new groups ofpatients that follow a trajectory corresponding to the intervention thatis being considered. If the patients in that new group have a lowerlikelihood, the system user obtains confidence in the proposed change.As another example, the hospital system can implement the change to anintervention for new patients. Over time, patient data will be collectedfor patients that follow the new protocol, which will generate a patientgroup that can be evaluated to determine whether the new group has aweaker trajectory towards the adverse outcome.

Trajectory to Physiological Progression Translation

Variables that are observable represent a chance to evaluate underlyingconditions. This enables a clinician to surmise information regarding apatient's health. In this regard, a clinician may or may not be able toobserve whether the patient actually has a corresponding condition. Forinstance, even where a clinician diagnoses a condition, there may beinsufficient information available to properly ascertain whether thepatient actually has the condition.

Aspects of the present disclosure herein define physiologicalprogressions, e.g., sequences of conditions/events/processes or otherphysiological characteristics that a patient progresses through on apath to an outcome (either adverse or favorable). The clinician may notbe able to observe the physiological progression, but the clinician cansee the outward effect of the physiological progression in the observed(likelihood, consequence) variables.

According to aspects of the present disclosure herein, a trajectory isdefined, based upon an analysis, e.g., clustering or otherwisesegmenting electronic clinical data patterns including patterns overtime. A trajectory corresponds to a path, e.g., a vector or othermeasure that allows patient data to be analyzed, clustered and otherwiseevaluated for trend analysis, similarity matching, etc. The system asdescribed more fully herein, can evaluate historical electronic clinicaldata where outcome information is available and compute for each patientor patient group in the historical information, a trajectory.

By way of illustration, a historical group of patients may be evaluatedto identify an observable trajectory where all of the patients followthe same temporal data pattern leading to an adverse outcome, such asacute kidney injury (AKI). Here, the trajectory is more than just asingle event defining the adverse outcome. Rather, the trajectoryincludes a time history of measures, e.g., symptoms, conditions, eventsor other observable aspects. However, this trajectory can be discernedfrom patterns in clustered or segmented patient data as described morefully herein.

Thus, electronic clinical data can be evaluated. A system user can studyvarious clusters/segments/groups of data to understand the key drivers(trajectories) that translate to physiological progressions thatultimately lead to an adverse outcome for those patients on thattrajectory. The system user can thus set out to evaluate what can bechanged in patient care to avoid certain trajectories, to detourpatients from certain trajectories, to reduce likelihood if a patient ison a certain trajectory, etc.

Miscellaneous Considerations:

With reference to the FIGURES generally, as noted in greater detailherein, care element progressions are identified by extractingelectronic clinical data associated with patients under evaluation.According to aspects of the present disclosure, the information thatcomprises the electronic clinical data (see for example, trajectory dataincluded in the electronic clinical data) can be derived from the datatypes corresponding to electronic patient data, the (likelihood,consequence) variables, the outcome (likelihood, consequence) models,attributions, etc.

In this regard, the electronic clinical data may be logically organizedin a temporal manner. For example, a time sequence of data variables canbe created from the electronic clinical data where the state of a valuefor each variable is represented for that time. In this regard, theintervals may be event based or otherwise event driven. Conceptually,this can be thought of as a table where the various “types” ofelectronic clinical data define the columnar fields used for analysis,and a time sequence defines the rows of the table. The intervals (rows)may be determined based upon events that cause the status of a variableto change.

For instance, in an example implementation, the creation of a new row isevent-based in that a new row is created any time new informationbecomes available for the patient. Each row is then date and timestamped with the time that the new information became available.Information for other variables is carried forward from the previousrecord unless it has expired. Thus, the creation of new records isevent-based but the records represent a time-stamped temporal series ofdata. Thus, the values of the records are the state/value of eachelectronic clinical data type at a given time, and accordingly, eachrecord represents a snapshot in time. Alternatively, the above can beconceptualized as an array of data elements extended as a vector thatrepresents time. This organization facilitates retrospective temporalanalysis of the electronic clinical data.

The above importance of temporally oriented data characterizationnotwithstanding, changes in the values or states of variables may beimportant to defining trajectories regardless of time at which thechanges occur. As stated elsewhere within, the values of staticvariables may be important as well. Thus trajectories will have staticelements, time-independent elements and temporally oriented elements.

Moreover, electronic clinical data can be derived from a subset of theabove data sources, e.g., by selecting key data types, by performingdata synthesis, data manipulation, etc. For instance, it may bedesirable to filter the available electronic clinical data down to thekey variables that are related to an outcome of interest.

Various aspects of the present invention are directed to identifyinginsights related to the occurrence of an outcome of interest. An exampleis an adverse health outcome such as acute kidney injury. However, theadverse outcome of interest may be defined to be the occurrence of oneor more of a set of adverse health outcomes, thus allowing the systemsand methods herein to be scaled to accommodate a wide range of healthanalytics.

Moreover, the systems and methods described herein may obtain electronicclinical data for new patients. Then using the systems and methods setout herein, the new patients may be assigned to previously-definedpatient groups to identify recommended clinical interventions for thenew patients based on previously-identified interventions for thepreviously-defined patient groups. Here, tracking the outcomesexperienced by the new patients after recommended clinical interventionshave been implemented may be used, for instance, for the purpose ofassessing the value of the recommendations in lowering the likelihood ofone or more adverse health outcomes of interest.

Root Cause Analysis:

According to still further aspects of the present disclosure,retrospective systemic root cause analysis is utilized to supportclinical analysis and may even be used to affect policy decision makingBasically, the root cause analysis uses retrospection of a population ofdata to draw conclusions across the population as to the likely rootcause(s) that lead to the eventual adverse outcomes identified withinthe patient data. As such, clinical policy decisions can be made, torespond to detected patterns.

Example Computer Implementation

Referring to FIG. 8, a block diagram of a data processing system isdepicted in accordance with the present disclosure. Data processingsystem 800 may comprise one or more processors 802 connected to systembus 804. Also connected to system bus 804 is memory controller/cache806, which provides an interface to local memory 808. An I/O bus 810 isconnected to the system bus 804 and provides an interface to I/O devices812, such as input output devices (I/O devices), storage, networkadapters, graphic adapters, etc.

Also connected to the I/O bus 810 may be devices such as one or morestorage devices 814 and a computer usable storage medium 816 havingcomputer usable program code embodied thereon. The computer usableprogram code may be executed, e.g., by the processor(s) 802 to implementany aspect of the present disclosure, for example, to implement anyaspect of any of the methods, processes and/or system componentsillustrated in FIGS. 1-7.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice, e.g., the system described with reference to FIG. 8. Thus, acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves through a transmission media.

Exemplary and non-limiting structures for implementing a computerreadable storage medium include a portable computer diskette, a harddisk, a random access memory (RAM), Flash memory, a read-only memory(ROM), a portable compact disc read-only memory (CD-ROM), digital videodisk (DVD), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. Each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer programinstructions, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Each block in the flowchart or block diagrams of the FIGURES herein, mayrepresent a module, segment, or portion of code, which comprises one ormore executable instructions for implementing the specified logicalfunction(s). However, the functions noted in the block may occur out ofthe order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Further, the terms “comprises” and “comprising,” when used inthis specification, specify the presence of stated features, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, steps, operations,elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of anymeans or step plus function elements in the claims below are intended toinclude any disclosed structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present disclosure has been presentedfor purposes of illustration and description, but is not intended to beexhaustive or limited to the disclosure in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of thedisclosure. The aspects of the disclosure herein were chosen anddescribed in order to best explain the principles of the disclosure andthe practical application, and to enable others of ordinary skill in theart to understand the disclosure with various modifications as aresuited to the particular use contemplated.

Having thus described the disclosure of the present application indetail and by reference to embodiments thereof, it will be apparent thatmodifications and variations are possible without departing from thescope of the disclosure defined in the appended claims.

What is claimed is:
 1. A computer-implemented method of evaluatingoutcomes, comprising: identifying a patient care-related outcome ofinterest; extracting electronic clinical data associated with historicalhealthcare encounters for a plurality of patients, by: including in theplurality of patients, a first subset of patients that experienced theoutcome of interest; and including in the plurality of patients, asecond subset of patients that did not experience the outcome ofinterest; deriving at least one model based upon model variables thathave a clinical and/or statistical significance to the outcome ofinterest the at least one model selected from: an outcome likelihoodmodel that estimates the likelihood of the outcome of interest using agroup of likelihood variables, and a consequence likelihood model thatestimates consequences of the outcome of interest using a group ofconsequence variables; determining patient groups among the plurality ofpatients for which electronic clinical data is extracted, with the aidof a computer processor that executes a program, by: defining eachpatient group by grouping together those patients having a similar datapattern present in their corresponding extracted electronic clinicaldata based upon at least one model variable: and selecting the datapatterns such that the defined patient groups differentiate from oneanother in terms of a likelihood of the outcome of interest,consequences associated with the outcome of interest or both based uponclinical data associated with a value of the at least one modelvariable; deriving a hypothesized etiological explanation for why one ormore patient groups have different likelihoods of the outcome ofinterest, consequences associated with the outcome of interest or both,when compared to other defined patient groups, by comparing definedpatient groups and identifying different likelihoods of the outcome ofinterest; identifying at least one physiological process associated withthe derived hypothesized etiological explanation; outputting a clinicalintervention based upon the identified physiological process; selectingthe clinical intervention for a select patient group wherein theidentified clinical intervention is directed to decrease the likelihoodof the adverse outcome of interest or decrease the consequences of theadverse outcome of interest, or both, for the select patient group; andverifying the effectiveness of the selected clinical intervention by:generating a patient group that follows a trajectory corresponding tothe selected clinical intervention; and determining a likelihood of theoutcome of interest for the generated patient group; and modifying theselected clinical intervention based upon the determined likelihood ofoutcome of interest for the generated patient group.
 2. The method ofclaim 1, wherein selecting the clinical intervention comprises selectingthe clinical intervention that is directed to prevent patients fromentering higher-likelihood patient groups, prevent patients fromentering higher-consequence patient groups, lower the likelihood of theadverse outcome for patients in higher-likelihood patient groups, lowerthe consequences of the adverse outcome for patients inhigher-consequence patient groups, or any combination of the foregoing.3. The method of claim 1, wherein identifying a patient care-relatedoutcome of interest comprises selecting a favorable outcome of interest,and further comprising: selecting the clinical intervention for a selectpatient group wherein the clinical intervention is directed to increasethe likelihood of the favorable outcome of interest or increase theconsequences of the favorable outcome of interest, or both, for theselect patient group.
 4. The method of claim 3, wherein selecting theclinical intervention comprises selecting the clinical intervention toassist patients in entering higher-likelihood patient groups, assistpatients in entering higher-consequence patient groups, increase thelikelihood of the favorable outcome for patients in lower-likelihoodpatient groups, increase the consequences of the favorable outcome forpatients in lower-consequence patient groups, or any combination of theforegoing.
 5. The method according to claim 1, wherein defining eachpatient group comprises creating at least one patient group as includingat least one patient from the first subset of patients and at least onepatient from the second subset of patients.
 6. The method according toclaim 1, wherein extracting electronic clinical data comprisesextracting likelihood variables or consequence variables or both,wherein the likelihood variables define variables associated with apatient's likelihood of having the outcome of interest and consequencevariables define variables associated with a patient's consequencesassociated with the outcome of interest.
 7. The method of claim 6,wherein: extracting likelihood variables comprises generating thelikelihood variables as a result of reconciliation of likelihood factorsidentified by outcome-specific etiological models with available patientdata; and extracting consequence variables comprises generating theconsequence variables as a result of reconciliation of consequencefactors identified by outcome-specific etiological models with availablepatient data.
 8. The method according to claim 6, wherein groupingtogether those patients having a similar data pattern present in theircorresponding extracted electronic clinical data comprises definingsimilar data patterns based upon the data values of a subset of thelikelihood variables, consequence variables or both, such that thepatient groups are defined in terms of variables and not in terms ofwhether or not the patient has experienced the outcome of interest. 9.The method according to claim 8 further comprising: converting thesubset of the likelihood variables, consequence variables or both, intodiscrete measures having a fixed number of value options; whereingrouping together those patients having a similar data pattern comprisegrouping together those patients having the same data values associatedwith the discrete measures.
 10. The method according to claim 1, whereingrouping together those patients having a similar data pattern presentin their corresponding extracted electronic clinical data comprisesgrouping together those patients having a data pattern that includesboth non-temporal and temporal data patterns.
 11. The method accordingto claim 1, wherein grouping together those patients having a similardata pattern present in their corresponding extracted electronicclinical data comprises identifying a common trajectory associated withat least one defined patient group where the trajectory represents adata pattern across a time history of changes in the physiological stateof patients, occurrences of events that patients experience, or acombination of states and events over time.
 12. The method according toclaim 1, wherein grouping together those patients having a similar datapattern present in their corresponding extracted electronic clinicaldata comprises defining a data pattern by defining values of one or morestatic variables that do not change during the course of a hospitalencounter and/or defining a pattern across a time history of changes inthe physiological state of patients, occurrences of events that thepatients experience, or both.
 13. The method according to claim 1,wherein defining each patient group comprises developing an outcomelikelihood scoring algorithm that characterizes the likelihood of theoutcome of interest as a function of likelihood variables derived fromextracted electronic clinical data and defining the patient groups basedon data patterns in the likelihood variables employed in the scoringalgorithm.
 14. The method of claim 13, further comprising configuringthe outcome likelihood scoring algorithm to define the likelihood of theoutcome of interest in terms of baseline and dynamic likelihoods. 15.The method according to claim 1, wherein defining each patient groupcomprises developing an outcome consequence scoring algorithm thatcharacterizes the consequences associated with the outcome of interestas a function of consequence variables derived from extracted electronicclinical data and defining the patient groups based on data patterns inthe consequence variables employed in the scoring algorithm.
 16. Themethod according to claim 1, wherein defining each patient groupcomprises clustering or segmenting the extracted electronic clinicaldata and defining a patient group for each data cluster or segment. 17.The method according to claim 1, wherein deriving a hypothesizedetiological explanation comprises deriving physiological processdescriptions that hypothesize a causal relationship between the datapatterns that define patient groups and the likelihood of occurrence ofand/or consequences associated with the outcome of interest forindividual groups.
 18. The method according to claim 1, wherein derivinga hypothesized etiological explanation comprises deriving thehypothesized etiological explanations based on clinical subject matterexpertise, based on the contents of a clinical knowledgebase, or by acombination of the two.
 19. The method according to claim 1, furthercomprising: identifying the clinical intervention for a select patientgroup wherein the clinical intervention is identified based on clinicalsubject matter expertise, based on the contents of a clinicalknowledgebase, or based on a combination of the two.
 20. The methodaccording to claim 1, further comprising: obtaining electronic clinicaldata for new patients; assigning the new patients to previously-definedpatient groups; and recommending one or more clinical interventions forthe new patients based on previously-identified interventions for thepreviously-defined patient groups.
 21. The method of claim 20, furthercomprising: applying the method iteratively over time to achievecontinuous improvement in patient care relative to the outcome ofinterest.
 22. The method of claim 20 further comprising: tracking theoutcomes experienced by the new patients after clinical interventionshave been recommended for the purpose of assessing the value of therecommendations in improving patient care relative to the outcome ofinterest.
 23. An apparatus, comprising: a processor coupled to a memory,wherein the processor is programmed to identify insights related tooutcomes by executing program code to: identify a patient care-relatedoutcome of interest; extract electronic clinical data associated withhistorical healthcare encounters for a plurality of patients, wherein:the plurality of patients include a first subset of patients thatexperienced the outcome of interest; and the plurality of patientsinclude a second subset of patients that did not experience the outcomeof interest; derive at least one model based upon model variables thathave a clinical and/or statistical significance to the outcome ofinterest the at least one model selected from: an outcome likelihoodmodel that estimates the likelihood of the outcome of interest using agroup of likelihood variables, and a consequence likelihood model thatestimates consequences of the outcome of interest using a group ofconsequence variables; define patient groups among the plurality ofpatients, wherein: each patient group is defined by grouping togetherthose patients having a similar data pattern present in theircorresponding extracted electronic clinical data based upon at least onemodel variable: and the data patterns are selected such that the definedpatient groups differentiate from one another in terms of a likelihoodof the outcome of interest, consequences associated with the outcome ofinterest or both based upon clinical data associated with a value of theat least one model variable; provide an interface for a user to derivehypothesized etiological explanations for why one or more patient groupshave different likelihoods of the outcome of interest, consequencesassociated with the outcome of interest or both, when compared to otherdefined patient groups; identify at least one physiological processassociated with the derived hypothesized etiological explanation; outputa clinical intervention based upon the identified physiological process;select the clinical intervention for a select patient group wherein theidentified clinical intervention is directed to decrease the likelihoodof the adverse outcome of interest or decrease the consequences of theadverse outcome of interest, or both, for the select patient group; andverify the effectiveness of the selected clinical intervention by:generating a patient group that follows a trajectory corresponding tothe selected clinical intervention; and determining a likelihood of theoutcome of interest for the generated patient group; and modify theselected clinical intervention based upon the determined likelihood ofoutcome of interest for the generated patient group.